Abstract
Altered functional connectivity has been reported in infants with prenatal exposure to opioids, which significantly interrupts and influences endogenous neurotransmitter/receptor signaling during fetal programming. Better birth outcomes and long-term developmental outcomes are associated with medication for opioid use disorder (MOUD) during pregnancy, but the neural mechanisms underlying these benefits are largely unknown. We aimed to characterize effects of prenatal opioid/other drug exposure (PODE) and the neural basis for the reported beneficial effects of MOUD by examining neonatal brain functional organization. A cohort of 109 human newborns, 42 PODE, 39 with prenatal exposure to drugs excluding opioids (PDE), 28 drug-free controls (males and females) underwent resting-state fMRI at 2 weeks of age. To examine neural effects of MOUD, PODE infants were separated into subgroups based on whether mothers received MOUD (n = 31) or no treatment (n = 11). A novel heatmap analysis was designed to characterize PODE-associated functional connectivity alterations and MOUD-related effects, and permutation testing identified regions of interest with significant effects. PODE neonates showed alterations beyond those associated with PDE, particularly in reward-related frontal-sensory connectivity. MOUD was associated with a significant reduction of PODE-related alterations in key regions of endogenous opioid pathways including limbic and frontal connections. However, significant residual effects in limbic and subcortical circuitry were observed. These findings confirm altered brain functional organization associated with PODE. Importantly, widespread normalization effects associated with MOUD reveal, for the first time, the potential brain basis of the beneficial effects of MOUD on the developing brain and underscore the importance of this treatment intervention for better developmental outcomes.
SIGNIFICANCE STATEMENT This is the first study to reveal the potential neural mechanisms underlying the beneficial effects on the neonate brain associated with MOUD during pregnancy. We identified both normalization and residual effects of MOUD on brain functional architecture by directly comparing neonates prenatally exposed to opioids with MOUD and those exposed to opioids but without MOUD. Our findings confirm altered brain functional organization associated with prenatal opioid exposure and demonstrate that although significant residual effects remain in reward circuitry, MOUD confers significant normalization effects on functional connectivity of regions associated with socioemotional development and reward processing. Together, our results highlight the importance of MOUD intervention for better neurodevelopmental outcomes.
Introduction
Paralleling the substantial increase in the use of prescribed and illegal opioids in recent years (Volkow and Blanco, 2021), the number of pregnant women using opioids has more than quadrupled in the United States between 1999 and 2014 (Haight et al., 2018). By directly acting on endogenous opioid receptor signaling pathways in the developing brain (Concheiro et al., 2010; Mactier and Hamilton, 2020), prenatal exposure to opioids influences fetal programming, which may cascade into a myriad of lasting effects across different domains of socioemotional processing, cognition, and behavior (Konijnenberg and Melinder, 2011; Conradt et al., 2019; Yeoh et al., 2019). Despite the abundance of behavioral studies on prenatal opioid exposure (Baldacchino et al., 2014; Lee et al., 2020; Nelson et al., 2020), few have examined the underlying neural basis (Sirnes et al., 2018; Radhakrishnan et al., 2021b) of its effects, especially during the critical infancy period most proximal to exposure (Salzwedel et al., 2015, 2020; Radhakrishnan et al., 2021a). In the fight to mitigate the adverse effects of opioid use disorder during pregnancy, medication for opioid use disorder (MOUD; with buprenorphine/methadone; Reddy et al., 2017) is a powerful tool to stabilize opioid levels that the fetus is exposed to, hence reducing repeated prenatal withdrawal cycles (Kaltenbach et al., 1998; Hudak et al., 2012). Accordingly, MOUD is associated with less severe symptoms of and lower risk for neonatal abstinence syndrome, higher gestational age, weight, and head circumference at birth, as well as better long-term developmental outcomes (Center for Substance Abuse Treatment, 2005; Jones et al., 2012; Kaltenbach et al., 2018; Substance Abuse and Mental Health Services Administration, 2018). However, the neural mechanisms underlying the reported benefits of MOUD remain largely unknown. This question holds great scientific and clinical significance and is best answered by directly comparing newborns prenatally exposed to MOUD with those exposed to opioids but without MOUD.
Emerging in utero and established at birth (Gao et al., 2009; Smyser et al., 2010; Thomason et al., 2013; Gilmore et al., 2018), the functional networks of the brain, measured by resting-state functional magnetic resonance imaging (rsfMRI), show sequential and patterned development during infancy (Gao et al., 2015). In addition to studies of typical brain development, studies of neonatal functional connectivity have demonstrated significant changes within and between limbic, subcortical, and prefrontal areas associated with prenatal cocaine, marijuana, and opioid exposures (Grewen et al., 2015; Salzwedel et al., 2015, 2016, 2020; Morie et al., 2019; Radhakrishnan et al., 2021b). As such, rsfMRI-based examination of functional connectivity development offers a unique window to investigate the brain basis of prenatal drug exposures as well as MOUD effects.
In this study, we examined a cohort of 109 newborns with 2-week rsfMRI scans including the following: (1) prenatal exposure to opioids and other drugs (PODE; n = 42), (2) prenatal exposure to 1 or more other drugs excluding opioids (PDE; n = 39), and (3) drug-free controls (CTRs; n = 28). A novel heatmap analysis characterized functional brain alterations associated with PODE as compared with CTR to better understand the neural basis of PODE effects in affected newborns. Importantly, we aimed to unveil potential neural mechanisms associated with the beneficial effects of MOUD by directly comparing functional connectivity development of 31 newborns with MOUD (PODE-T) and 11 without MOUD in the PODE group (PODE-NT). We had three specific hypotheses. First, consistent with reported effects of PODE in newborns (Salzwedel et al., 2020; Radhakrishnan et al., 2021a) and older children (Sirnes et al., 2018; Radhakrishnan et al., 2021b), we expected to observe functional connectivity alterations in reward-related areas in the PODE group. Second, given the stabilization effects of MOUD to minimize detrimental effects of prenatal withdrawal (Reddy et al., 2017), we hypothesized significant reduction of functional connectivity alterations in PODE-T versus PODE-NT infants. Third, given that both illicit and MOUD opioids act on the same opioid signaling pathways (Darcq and Kieffer, 2018), we still expected to see residual effects of opioid exposure even after MOUD. If validated, these findings could significantly improve our understanding of the brain basis for the effects of PODE and MOUD.
Materials and Methods
Participants
Participants in this study were enrolled as part of a longitudinal project examining the impact of prenatal drug exposures on early brain development. Infants were assigned to cohorts based on drug exposure (inclusionary criteria described below) including the following: (1) PODE, (2) PDE, and (3) CTR. To examine the potential effects of MOUD, the PODE group was further separated into subgroups based on whether mothers received MOUD (PODE-T, including opioids and MOUD exposure, i.e., buprenorphine, methadone) or no treatment (PODE-NT, including opioids and no MOUD exposure). A comprehensive review of self-report, urine toxicology screen, and medical records was conducted to determine classification of different participant groups to minimize potential report errors/biases (described below). Informed consent was obtained from parents/legal guardians of infant participants under protocols approved by University of North Carolina (UNC) at Chapel Hill and Cedars-Sinai Biomedical Institutional Review Boards.
At 2 weeks of age, 169 infants (44 CTR, 60 PDE, 65 PODE; PODE subgroups, 45 PODE-T, 20 PODE-NT) underwent rsfMRI; 52 infants (13 CTR, 19 PDE, 20 PODE; PODE subgroups, 13 PODE-T, 7 PODE-NT) did not successfully complete the rsfMRI scan (i.e., woke up before or during this scan). Data from eight infants (two CTR, three PDE, three PODE; PODE subgroups, one PODE-T, two PODE-NT) did not pass quality control during data preprocessing (as described below) and were also excluded, yielding a final sample of 109 infants (28 CTR, 39 PDE, 42 PODE; PODE subgroups, 31 PODE-T, 11 PODE-NT; both males and females included; Table 1). Drug-exposed cohorts were matched on sex, race, gestational age at birth, gestational age at scan, socioeconomic status as determined by the Area Deprivation Index (ADI; state decile and national percentile; https://www.neighborhoodatlas.medicine.wisc.edu/; Kind and Buckingham, 2018), and the number of volumes remaining after scrubbing (as described below; Table 1). The PODE group had significantly lower birthweight, lower levels of maternal education, and elevated maternal depression as indexed by the Edinburgh Postnatal Depression Scale (EPDS; Murray and Carothers, 1990) compared with CTR and PDE infants. In addition to the number of volumes remaining after scrubbing, these were included as covariates of no interest in all analyses to control for group differences in these variables.
Subject demographics
Table 1-1
Feeding Information. Download Table 1-1, DOCX file.
Table 1-2
Inclusionary criteria for drug-based cohorts. Download Table 1-2, DOCX file.
Table 1-3
PDE/PODE profiles. Download Table 1-3, DOCX file.
Recruitment and exclusionary criteria
Pregnant women were recruited in the third trimester of pregnancy. Primary recruitment sites for the drug-exposed groups were local residential and outpatient treatment programs for women with perinatal substance use disorders and their children. In addition, we recruited mothers from the health departments obstetric clinics of Chatham, Orange, Durham, Alamance, and Wake counties; the University of North Carolina hospital low-income obstetrics clinic; and from flyers, local advertisements, and Craigslist. At enrollment, mothers were required to be at least 18 years of age, with singleton pregnancy, and free from (1) chronic medical or psychiatric disease, (2) untreated current clinical depression or anxiety disorder, and (3) a language barrier that might prevent informed consent. The infants took part in the imaging experiment during the neonatal period (2–6 weeks of age). All infants were living with biological mothers at time of testing. Exclusionary criteria for infants included gestational birthweight <4.75 pounds, delivered at <32 weeks gestation, history of mechanical ventilation or surgery of any kind, >24 h in neonatal intensive care unit, or chronic illness of any kind. Exclusionary criteria did not include any stipulations on feeding methods after birth. However, assessments collected at the infant's 2-week visit surveying feeding methods from the previous week reflected a range of feeding methods including the following: (1) breastmilk only (either breastfed or fed pumped breastmilk), (2) infant formula only, and (3) breastmilk and infant formula (Extended Data Table 1-1). Feeding methods for infants with PDE/PODE are known to be highly variable (LaGasse et al., 2003; Bogen et al., 2017). Although breastmilk (either through breastfeeding and/or feeding with pumped milk) is associated with maternal and infant health benefits (Eidelman and Schanler, 2012), including reduced rates and severity of neonatal abstinence syndrome (Abdel-Latif et al., 2006; Welle-Strand et al., 2013a), mothers with opioid use disorder frequently encounter breastfeeding obstacles related to various psychosocial and environmental factors (Krans et al., 2015; Bogen and Whalen, 2019). Importantly, the majority of infants in each group were fed breastmilk only or in combination with infant formula (CTR, 81.48%; PDE, 68.42%; PODE, 68.57%; Extended Data Table 1-1).
Drug inclusionary criteria
PDE/PODE status was based on (1) maternal self-report on enrollment of the study, (2) maternal prenatal urine toxicology at study visits, (3) medical record queries of infant toxicology screen (urine or meconium), and (4) medical record queries of maternal urine toxicology screen during pregnancy. Given the short half-life of most substances and related metabolites, urine toxicology is limited to a short window of detection (i.e., detecting recent use only; Yonkers et al., 2011). Thus, maternal self-report, positive urine toxicology for either mother or infant, or positive meconium toxicology for infant qualified the mother-infant dyad for PDE/PODE status (inclusionary criteria summarized in Extended Data Table 1-2).
The distribution of drug exposures revealed a predominance of polydrug use; 64 (22 PDE, 11 PODE-NT, 31 PODE-T) of 81 (39 PDE, 11 PODE-NT, 31 PODE-T; i.e., 79.01%) neonates were exposed to >1 drug type, and 44 unique polydrug profiles were identified (Extended Data Table 1-3). Women using opioids while pregnant are more likely to use other substances such as alcohol, nicotine, depressants, and stimulants compared with women who are not using opioids (Jones et al., 2009; Winklbaur et al., 2009; Heberlein et al., 2012; Azuine et al., 2019). This was reflected in our sample in that we were unable to match for polydrug use between the PDE and PODE groups (Table 1). Importantly, however, PODE-NT and PODE-T groups did not differ on use of any reported or tested nonopioid substances (cocaine, nicotine, alcohol, marijuana, stimulants, depressants).
Data acquisition
Participants were fed, swaddled, and fitted with ear protection before imaging. All subjects were in a natural sleep state during the imaging session. Head position was secured in the scanner using a vacuum-fixation device. Vital signs (heart rate, SaO2) were monitored continuously throughout the examination. Data were collected on a Siemens 3T Prisma with 32-channel head coil. T1-weighted structural images were collected using a 3D magnetization-prepared rapid acquisition gradient echo pulse sequence (MPRAGE; TR = 2400 ms, TE = 2.22 ms, inversion time = 1000 ms, voxel size = 0.8 mm isotropic, field of view = 256 × 256 mm). Resting-state fMRI data were acquired using a T2*-weighted echo planar imaging (EPI) pulse sequence (TR = 800 ms, TE = 37 ms, 72 slices, voxel size = 2 mm isotropic, multiband factor = 8, field of view = 208 × 208 mm, 840 volumes).
Data preprocessing
Functional data were preprocessed using the Functional MRI of the Brain Software Library (FSL; Jenkinson et al., 2012), Analysis of Functional Neuroimages (AFNI; Cox, 1996), and MATLAB software (R2019a). Steps included discarding the initial three volumes, EPI distortion correction (FSL functions topup and applywarp; Andersson et al., 2003; Smith et al., 2004), motion correction, motion censoring [i.e., volumes with framewise displacements (FD) >0.3 mm were removed, or scrubbed, from the data; one volume immediately preceding and two volumes following the scrubbed volume were also removed; Power et al., 2012], interpolation of censored time points, and bandpass filtering (0.01–0.08 Hz). Subjects with <225 volumes postscrubbing were excluded; subjects with at least 225 volumes postscrubbing were included, and the full range of all available data (i.e., all remaining volumes) was used in the analyses. The average length of the BOLD time series used for all infants was 583.35 volumes, which is equivalent to 7.78 min of data (CTR, 540.39 volumes = 7.21 min; PDE, 581.56 volumes = 7.75 min; PODE, 613.64 volumes = 8.18 min; PODE-NT, 585.91 volumes = 7.81 min; PODE-T, 623.48 volumes = 8.31 min; no significant differences in the number of volumes remaining after scrubbing between any groups; Table 1). The number of volumes remaining after scrubbing was included as a covariate of no interest in all subsequent analyses. Confound regression was used to reduce distance dependence (Ciric et al., 2017). Specifically, the confound regression strategy consisted of motion censoring plus a 32-parameter nuisance signal model, eight regressors [i.e., eroded white matter (WM), eroded CSF, and six parameters corresponding to rigid-body motion correction], their derivatives, quadratic terms, and squares of derivatives. Nuisance signals were also bandpass filtered to prevent frequency-dependent mismatch (Hallquist et al., 2013). Nuisance regression was performed via linear regression (AFNI function 3dTproject). Censored time points were ignored to not influence the fit and were then excised from the data. Finally, the global signal was extracted using a whole-brain mask, excluding the eroded WM and CSF regions, and regressed from the data. The UNC neonate template was used for coregistration (Shi et al., 2011). Spatial normalization was achieved using nonlinear registration between each subject's high-resolution anatomic scan to the standard template as well as nonlinear functional-to-anatomic alignment and functional-to-standard registration (all using Advanced Normalization Tools; Avants et al., 2008). Finally, the data were spatially smoothed using a Gaussian kernel of 6 mm full-width at half-maximum. Functional data were resampled to a voxel size of 4 mm3 to optimize computation time for the data-driven heatmap and permutation analyses.
Data analysis
Whole-brain heatmap analysis
Functional connectivity measures were derived using a neonate functional parcellation-based atlas (UNC-CEDARS INFANT; Shi et al., 2018). For each seed region of interest (ROI; n = 223), the average residual time series was extracted and correlated with every other voxel in the whole brain. Next, voxelwise linear regression was conducted to detect significant functional connectivity differences between the drug-based cohorts. Other participant characteristics were included as covariates of no interest in the model including birthweight, volumes remaining after scrubbing, maternal education, and maternal depression (as indexed by the EPDS). We used two initial thresholding strategies. First, a voxelwise p value (p < 0.05) thresholding strategy was used. A summary measure defined as the percentage of connections showing significant differences (at p < 0.05) was calculated and assigned to the seed ROI. Second, to avoid sample size biases among the subgroups analyzed (i.e., findings were not driven by the uneven distribution of subjects across the subgroups analyzed; e.g., PODE-NT group had the smallest N), a bias-corrected Hedge's g effect size was calculated (Nakagawa and Cuthill, 2007; Hentschke and Stüttgen, 2011; Gerchen et al., 2021), and a medium effect size (g ≥ 0.5) was used as a threshold (Reddan et al., 2017; i.e., connections showing a medium or large effect size were included). A summary measure defined as the percentage of connections showing at least a medium effect size was calculated and assigned to the seed ROI. This process was repeated for all ROIs to generate a heatmap. Using this approach, pairwise effects (i.e., PODE vs CTR, PODE-NT vs CTR, PODE-T vs CTR, PODE-T vs PODE-NT) were characterized in the resulting heatmaps. It is important to note that the functional parcellation-based heatmaps were used (1) to provide a qualitative overview of general distribution of pairwise effects and (2) as a data-driven screening to identify ROIs showing significant effects following permutation testing (described below).
A post hoc analysis using all brain voxels as ROIs was also used to generate a separate set of heatmaps for visualization. Specifically, a functional seed centered on each voxel (with its six face-connecting neighboring voxels) was defined, and its connections to all other voxels across the whole brain were assessed using linear regression to quantify the percentage of connections of this voxel showing significant differences between the two groups. This process was repeated for all voxels to generate a heatmap with p-value-based thresholding (p < 0.05). These heatmaps were generated for visualization purposes only for qualitative comparison with the spatial distribution of the functional parcellation heatmaps.
ROI-level analysis
To elucidate the underlying functional connectivity patterns displaying PODE effects (PODE vs CTR) and MOUD effects (PODE-T vs PODE-NT), the functional parcellation heatmaps for these pairwise comparisons were used as a data-driven screening to identify ROIs showing significant effects after permutation testing. ROI-level analysis was then conducted on the selected ROIs to identify significant patterns of connectivity showing between-group differences in the network of the ROI.
Permutation testing (Nichols and Holmes, 2002) with random group assignments was conducted for each heatmap (PODE vs CTR and PODE-T vs PODE-NT). A null distribution of 10,000 permutations was generated by randomly shuffling the group assignment of each subject in the pairwise comparison and calculating the summary measure (i.e., percentage of connections showing significant differences at p < 0.05) for each ROI. In other words, for each ROI, the p-value-based summary measure computed with the correctly labeled data (i.e., percentage of connections showing significant differences at p < 0.05) was compared against the null distribution. A permuted p value (pperm) was calculated for each ROI (i.e., number of permutations in the null distribution with a larger summary measure compared with the summary measure computed with the correctly labeled data divided by the total number of permutations). ROIs that met a threshold cutoff of pperm < 0.05 were included in subsequent ROI-level analyses to further investigate the pattern of functional connectivity differences. Using the previously computed linear regression model for each ROI, significance was defined using a clustering approach (AFNI function 3dClustSim) to robustly correct for multiple comparisons. We used conservative settings (Eklund et al., 2016; Cox et al., 2017a,b) to achieve the desired correction rate of α = .05. Specifically, we imposed a voxelwise cutoff of p < 0.001 and generated smoothness estimates from the preprocessed data using the mixed-model autocorrelation function. Cluster sizes (bisided, nearest neighbor = 1) were established for each subsample. For each identified cluster, the mean connectivity z scores were extracted, and post hoc comparisons (using pairwise t tests) were performed to detect significance pairwise differences among all relevant groups. More specifically, for opioid-specific effects (PODE vs CTR), the PDE group was included in the post hoc comparisons to determine the extent to which the effects observed were specific to opioid exposure (i.e., PODE effects, pairwise between CTR, PDE, PODE). For effects associated with MOUD (PODE-T vs PODE-NT), the CTR and PDE groups were included in the post hoc comparisons (i.e., MOUD effects, pairwise between CTR, PDE, PODE-T, PODE-NT). This revealed patterns of normalization (i.e., PODE-T showing reduced differences with CTR) and residual effects. To quantify the magnitude of normalization toward the CTR group, the average difference in connectivity was calculated between PODE-NT and CTR as well as between PODE-T and CTR for each significant cluster showing this pattern. The percent reduction was calculated between the average connectivity differences (i.e., percentage difference in the degree of normalization toward connectivity levels observed in CTR). Similarly, to quantify the magnitude of partial normalization toward the PDE group, the average difference in connectivity was calculated between PODE-NT and PDE as well as PODE-T and PDE for each significant cluster showing this pattern. The percentage reduction was calculated between the average connectivity differences (i.e., percentage difference in the degree of overcorrection compared with connectivity levels observed in PDE).
Results
Pairwise heatmap effects
Functional parcellation-based heatmaps from the first-level analysis demonstrating pairwise differences between PODE and CTR, PODE-NT and CTR, PODE-T and CTR, as well as PODE-T and PODE-NT are shown in Figure 1 and Extended Data Figure 1-1. It is important to note that these heatmaps were used as a data-driven screening to identify ROIs showing significant effects following permutation testing (described below) but are shown here to provide a qualitative overview of the general distribution of pairwise effects. At the uncorrected level, the PODE group showed widespread differences in subcortical, limbic, frontal, and temporal areas (Fig. 1A, left). PODE-NT infants showed differences compared with CTR in subcortical, occipital, sensorimotor, frontal, and parietal regions (Fig. 1A, middle left). Differences for the PODE-T group were localized to subcortical, limbic, and frontal areas (Fig. 1A, middle right). By directly comparing PODE-T and PODE-NT infants, regions that were differentially affected by MOUD included subcortical, limbic, occipital, and frontal regions (Fig. 1A, right). Despite the difference in sample size (31 PODE-T, 11 PODE-NT), a similar degree of alterations was observed when comparing each PODE subgroup with CTR based on p-value thresholds (Fig. 1A); this is striking and indicates that the differences observed in PODE-NT are likely associated with much larger effect sizes. Indeed, by directly thresholding based on bias-corrected Hedge's g effect size (Fig. 1B; Extended Data Fig. 1-1B), differences observed in PODE-NT revealed much larger effect sizes compared with CTR as well as with PODE-T. The post hoc voxelwise heatmap analysis revealed a similar distribution of connectivity differences for all pairwise comparisons (Extended Data Fig. 1-2), providing qualitative validation for the connectivity patterns observed in the functional parcellation-based heatmaps.
Heatmaps of functional connectivity alterations. A, Qualitatively, pairwise difference heatmaps show widespread differences in patterns of neonatal functional connectivity between CTR and the opioid-exposed groups as well as a direct comparison between PODE-NT and PODE-T infants. The color map and the value of each ROI indicate the percentage of the connections this particular ROI (to every other voxel across the whole brain), showing significant differences between the two specified groups based on p-value thresholding (p < 0.05). B, Thresholding based on bias-corrected Hedge's g effect size resulted in a similar topology (compared with p threshold results). The color map and the value of each voxel indicate the percentage of the connections of this particular ROI (to every other voxel across the whole brain), showing significant differences between the two specified groups based on bias-corrected Hedge's g effect size thresholding (g ≥ 0.5, independent of sample size). Extended Data Figure 1-1 shows the axial view, and Extended Data Figure 1-2 shows voxelwise patterns. PODE: prenatal exposure to opioids and other drugs; PDE: prenatal exposure to 1 or more other drugs excluding opioids; CTR: drug-free controls; PODE-T: PODE with medication for opioid use disorder (MOUD) treatment; PODE-NT: PODE without MOUD treatment.
Figure 1-1
Heatmaps of functional connectivity alterations: Axial view. A, Qualitatively, pairwise difference heatmaps show widespread differences in patterns of neonatal functional connectivity between CTR and the opioid-exposed groups as well as a direct comparison between PODE-NT and PODE-T infants. The color map and the value of each ROI indicate the percentage of connections of this particular ROI (to every other voxel across the whole brain), showing significant differences between the two specified groups based on p-value thresholding (p < 0.05). B, Thresholding based on bias-corrected Hedge's g effect size resulted in a similar topology (compared with p threshold results). The color map and the value of each voxel indicate the percentage of the connections of this particular ROI (to every other voxel across the whole brain), showing significant differences between the two specified groups based on bias-corrected Hedge's g effect size thresholding (g ≥ 0.5, independent of sample size). Download Figure 1-1, TIF file.
Figure 1-2
Heat maps of functional connectivity alterations at the voxel-wise level. Heat maps derived from voxel-based regions of interest (i.e., functional seed centered on each voxel with its 6 face-connecting neighboring voxels) show a similar distribution of connectivity differences for all pairwise comparisons to heatmaps derived from functional parcellation-based regions of interest. Download Figure 1-2, TIF file.
PODE effects: ROI-level functional connectivity alterations in PODE compared with CTR
Permutation testing of the pairwise heatmap of PODE effects (i.e., PODE vs CTR) revealed six ROIs that met threshold criteria of pperm < 0.05 (Extended Data Fig. 2-1); of these, one ROI revealed significant between-group differences surviving cluster correction (Table 2). CTR infants showed increased frontal-sensory connectivity between right rectus gyrus and right visual cortex compared with the PODE group (Fig. 2). A post hoc analysis including the PDE group revealed a gradient effect whereby the functional connectivity alterations observed in PODE infants were above and beyond those in the PDE group, so PODE infants showed the most extreme differences compared with CTR. Furthermore, a post hoc analysis separating the PODE group into MOUD-based subgroups revealed no significant differences between the PODE subgroups, and both subgroups were significantly different from CTR infants (Extended Data Fig. 2-2). This further validated that the difference in connectivity between right rectus gyrus and right visual cortex was shared by the two subgroups.
Peak coordinates of ROI-level between-group differences for CTR versus PODE
Table 2-1
AAL Regions Used in Cluster Descriptions. Download Table 2-1, DOCX file.
ROI-level PODE effects. CTR infants showed greater frontal-sensory connectivity compared with the PODE group between right rectus gyrus (part of the medial orbitofrontal cortex) and right visual cortex. Post hoc analyses including the PDE group revealed a gradient effect in that PODE infants showed the most extreme differences compared with the CTR group, with PDE showing intermediate effects. Extended Data Figure 2-1 shows results following permutation testing, and Extended Data Figure 2-2 shows a post hoc analysis separating the PODE group into MOUD-based subgroups. R REC, Right rectus gyrus. *p < .05, **p < .01.
Figure 2-1
ROI-level PODE effects: Permutation testing. Permutation testing of the pairwise heatmap of PODE effects (PODE vs CTR) revealed six ROIs that met threshold criteria of pperm < 0.05. The red line indicates the true value of the summary measure computed with the correctly labeled data (i.e., percentage of connections showing significant differences at p < 0.05 for the given ROI). L FFG, Left fusiform gyrus; R PAL, right pallidum (i.e., basal ganglia); R REC, right rectus gyrus; L TPOmid, left temporal pole (middle); L PUT, left putamen; R STG: right superior temporal gyrus. Download Figure 2-1, TIF file.
Figure 2-2
ROI-level PODE effects: PODE group separated out into subgroups. CTR infants showed greater frontal-sensory connectivity compared with the PODE group between right rectus gyrus (part of the medial orbitofrontal cortex) and right visual cortex. Post hoc analyses including the PDE group revealed a gradient effect in that PODE infants showed the most extreme differences compared with the CTR group, with PDE showing intermediate effects. Furthermore, separating the PODE group into MOUD subgroups (PODE-T and PODE-NT) confirmed that, as expected, there were no significant differences between the PODE subgroups. In other words, this difference in connectivity is specific to the PODE group overall in that neither subgroup shows normalization with respect to the CTR infants. R REC, Right rectus gyrus. Download Figure 2-2, TIF file.
MOUD effects: ROI-level functional connectivity alterations in PODE-T compared with PODE-NT
Permutation testing of the pairwise heatmap of treatment-specific effects (i.e., PODE-T vs PODE-NT) revealed eight ROIs that met threshold criteria of pperm < 0.05 (Extended Data Fig. 3-1); of these, six ROIs revealed significant between-group connectivity differences surviving cluster correction (Table 3). Except for one connection where neither PODE-T nor PODE-NT showed significant differences when compared with CTR (Extended Data Fig. 3-2), MOUD effects followed two general normalization patterns as revealed by post hoc analyses incorporating the CTR and PDE groups. First, in one set of three connections, PODE-T infants exhibited normalization effects compared with PODE-NT infants when compared with CTR (PODE-NT ≠ PODE-T ∼CTR; Fig. 3A,B). These normalization effects were enriched in higher order connectivity involved in sensory integration (frontal-sensory, Fig. 3A) and corticolimbic regulation (limbic-parietal, Fig. 3B). Across these significant connections, the PODE-T group demonstrated a greatly reduced magnitude in the average connectivity difference with CTR infants compared with PODE-NT (average difference between PODE-T and CTR = 0.04 vs average difference between PODE-NT and CTR = 0.17, representing a 74.94% reduction; Fig. 3C). A gradient effect was also observed with respect to the PDE group, whereby PODE-T infants exhibited a reduced magnitude in the average connectivity difference with PDE infants compared with PODE-NT (average difference between PODE-T and PDE = 0.07 vs average difference between PODE-NT and PDE = 0.14, representing a 50.46% reduction; Fig. 3D).
Peak coordinates of ROI-level between-group differences for PODE-T versus PODE-NT
ROI-level MOUD effects: Normalization. A–D, PODE-T infants exhibited normalization effects compared with PODE-NT infants (PODE-NT ≠ PODE-T ∼CTR) in higher order frontal-sensory (A) and limbic-parietal (B) connectivity. Across these significant connections, PODE-T infants showed a reduced magnitude (74.94%) in the average connectivity difference with CTR infants compared with PODE-NT (C) as well as a reduced magnitude (50.46%) in the average connectivity difference with PDE infants compared with PODE-NT (D). Extended Data Figure 3-1 shows results following permutation testing. and Extended Data Figure 3-2 shows one connection where neither MOUD subgroup showed significant differences compared with CTR. R IFGoperc, Right inferior frontal gyrus (pars opercularis); R INS, right insula. *p < .05, **p < .01, ***p < .001.
Figure 3-1
ROI-level MOUD effects: Permutation testing. Permutation testing of the pairwise heatmap of MOUD effects (PODE-T vs PODE-NT) revealed eight ROIs that met threshold criteria of pperm < 0.05. The red line indicates the true value of the summary measure computed with the correctly labeled data (i.e., percentage of connections showing significant differences at p < 0.05 for the given ROI). R THA, Right thalamus; R SOG, right superior occipital gyrus; R IFGoperc, right inferior frontal gyrus (pars opercularis); R INS, right insula; L MCG, left middle cingulate gyrus; L THA, left thalamus; R MCG, right middle cingulate gyrus; R HIP, right hippocampus. Download Figure 3-1, TIF file.
Figure 3-2
ROI-level MOUD effects. In this limbic-frontal connection between right hippocampus and left frontal regions, neither PODE-T nor PODE-NT infants showed significant differences compared with the CTR group. R HIP, Right hippocampus. Download Figure 3-2, TIF file.
In another set of three connections, PODE-T infants displayed a different type of effect featuring two subpatterns (Fig. 4). First, PODE-T seemed to reverse the directional differences between PODE-NT and CTR (i.e., all positive differences between PODE-NT and CTR reversed to negative ones between PODE-T and CTR, and vice versa) resulting in slightly reduced overall degrees of differences between the two subgroups and CTR among these three connections (i.e., partial normalization toward the CTR group: average difference between PODE-T and CTR = 0.10 vs average difference between PODE-NT and CTR = 0.12, representing a 15.36% reduction; Fig. 4D). This effect was observed in subcortical (subcortical-frontal, Fig. 4A) and corticolimbic connectivity (limbic-temporal, Fig. 4B,C). Second, PODE-NT significantly differed from PDE in corticolimbic connectivity (limbic-temporal, Fig. 4B,C), but these differences were largely neutralized by MOUD (i.e., not significantly different between PODE-T and PDE), resulting in an overall normalization effect toward the PDE group (average difference between PODE-NT and PDE = 0.15 vs average difference between PODE-T and PDE = 0.08, representing a 49.42% reduction; Fig. 4E).
ROI-level MOUD effects: Residual effects. A–E, PODE-T infants exhibited residual effects (i.e., partial normalization) compared with PODE-NT infants relative to the CTR group in subcortical-frontal (A) connectivity as well as overcorrection effects compared with PODE-NT infants relative to the PDE group (PODE-NT ≠ PODE-T ∼PDE) in limbic-temporal (B, C) connectivity. Across these significant connections, PODE-T infants showed a small reduction (15.36%) in the magnitude of average connectivity difference with CTR infants compared with PODE-NT (D) as well as a reduced magnitude (46.42%) in the average connectivity difference with PDE infants compared with PODE-NT (E). Extended Data Figure 3-1 shows results following permutation testing. R THA, Right thalamus; L MCG, left middle cingulate gyrus; R MCG, right middle cingulate gyrus. *p < .05, **p < .01, ***p < .001.
Discussion
Our findings confirmed significant functional connectivity alterations in newborns with PODE in regions involved in reward processing. More importantly, direct comparisons of neonates of mothers with and without MOUD (with matched exposure to non-MOUD opioids and other drugs) revealed both full and partial normalization effects in the MOUD-treated group. To our knowledge, this is the first study to reveal the potential brain basis of the reported beneficial effects of MOUD on developmental outcomes (Kaltenbach et al., 2018; Conradt et al., 2019). The residual effects shared by PODE-T and PODE-NT newborns are consistent with known opioid effects on the fetal brain and long-term unfavorable developmental outcomes when comparing children exposed to MOUD prenatally and those with no prenatal drug exposure (Konijnenberg and Melinder, 2011; Yeoh et al., 2019).
Atypical functional connectivity in PODE
Endogenous opioids contribute to the formation and function of the developing fetal brain, playing a complex and crucial role with effects on neuronal survival, axonal integrity, oligodendrocyte maturation, and myelination (Vestal-Laborde et al., 2014; Conradt et al., 2018), all of which have cascading effects on the development of functional connectivity. It is therefore not surprising that exogenous opioids, which cross the placental and blood–brain barriers to act directly on endogenous opioid receptors in the fetal brain (Concheiro et al., 2010), significantly alter neural and behavioral development (Konijnenberg and Melinder, 2011; Goldfarb et al., 2020). Consistent with prior studies demonstrating that opioids interrupt and significantly influence functional networks in the brain with lasting consequences (Moningka et al., 2019; Radhakrishnan et al., 2021b), we observed significant opioid-specific effects with PODE infants showing decreased integrative frontal-sensory connectivity between right rectus and bilateral visual cortex compared with CTR. In particular, the right rectus is part of the medial orbitofrontal cortex, which is a key reward processing center in the brain (Rolls et al., 2020). Our findings are consistent with the fact that opioids act on µ-opioid receptors (Volkow et al., 2019b), which are enriched in orbitofrontal regions (Wager et al., 2007) involved in reward circuitry (Contet et al., 2004). Furthermore, altered connectivity with visual regions is in line with previous work linking prenatal MOUD exposure with altered visual maturation as measured by flash visual evoked potentials throughout the first 6 months (Whitham et al., 2010; McGlone et al., 2013, 2014). These effects may lead to long-lasting atypical visual development, with a high incidence of nystagmus (Gupta et al., 2012) and strabismus (Gill et al., 2003) observed in MOUD-exposed infants and oculomotor impairments lasting through late childhood (Sundelin Wahlsten and Sarman, 2013; Mactier and Hamilton, 2020).
We previously found that opioid-specific effects represent the highest proportion of effects among all illicit drugs (i.e., compared with marijuana and cocaine) in neonates exposed to MOUD and/or non-MOUD opioids (Salzwedel et al., 2020). The results in this study support and extend these prior findings, demonstrating that connectivity involved in higher-order cognitive processes such as social reward and integration are significantly disrupted in PODE infants at birth. Furthermore, post hoc analyses revealed that PODE infants showed a larger magnitude of altered functional connectivity compared with the PDE group, indicating that the use of opioids specifically, together with the greater number of other drugs used within the PODE group (prenatal exposure to six of seven drugs was more frequently observed in PODE than PDE; Table 1), resulted in functional connectivity differences that were more widespread and of higher magnitude, as we expected.
Normalization and residual effects of MOUD
Intriguingly, PODE-T infants showed a significant and dramatic normalization effect against functional alterations when compared with CTR. Specifically, three connections showing significant differences between PODE-NT and CTR were nonsignificant when comparing PODE-T with CTR. This normalization effect resulted in a 74.94% reduction in the connectivity differences among the connections compared with CTR as well as a 50.46% reduction in the connectivity differences compared with PDE. Buprenorphine and methadone were the two MOUD opioids in the PODE-T group, although buprenorphine dominated (30/31 PODE-T exposed to buprenorphine). As a partial µ-opioid agonist, buprenorphine binds to opioid receptors with higher affinity but lower activity than complete agonists like methadone and heroin (Reddy et al., 2017; Darcq and Kieffer, 2018). However, both MOUD drugs have a significantly longer half-life compared with illicit opioids (Reddy et al., 2017), therefore helping to stabilize maternal opioid levels. This may dramatically mitigate the detrimental effects of repeated withdrawal episodes for the fetus (Kaltenbach et al., 1998), potentially allowing for more normative development in networks where µ-opioid receptors are highly expressed. Indeed, prenatal morphine exposure disrupts neural maturation by increasing apoptosis in rat (Wang and Han, 2009) and human (Hu et al., 2002) fetal microglia and neurons, but treatment with MOUD opioids rescues this effect (Hu et al., 2002). In our sample, MOUD normalized connectivity patterns involved in higher order cognitive processes, including attention (frontal-sensory) and emotion/behavioral regulation (limbic-parietal, limbic-frontal), all of which are important for identifying and processing social reward, which is critical for learning and social bonding during infancy (Redcay and Warnell, 2018). Our results show that these connections are affected in PODE-NT but rescued with MOUD, which may provide a potential mechanism for how opioids and µ-opioid receptors mediate social reward (Volkow et al., 2019b). Whereas PODE-NT infants already showed altered functional connectivity of networks associated with these functions at birth, PODE-T had a significant normalization effect on these same networks, which could potentially cascade into improved neurobehavioral outcomes, as previously reported (Center for Substance Abuse Treatment, 2005; Jones et al., 2012; Kaltenbach et al., 2018; Substance Abuse and Mental Health Services Administration, 2018; Conradt et al., 2019). In addition to these medication effects, PODE-T infants may also have benefited from the tangible and social supports to mothers that may accompany pharmacological treatments (Dugosh et al., 2016). Lifestyle changes and access to services related to recovery may have improved fetal brain development, although PODE-T and PODE-NT infants did not differ in birthweight or gestational age at birth. Furthermore, the PODE-T and PODE-NT subgroups were fully matched on all other drug use, indicating that the normalization effects observed were not because of confounding effects of other drugs. The reported improvements in birth/developmental outcomes in children prenatally exposed to MOUD versus no treatment (Kaltenbach et al., 2018; Conradt et al., 2019), combined with these new findings of normalization effects of MOUD on brain functional connectivity, provide strong support for MOUD as not only an essential treatment for maternal opioid use disorder during pregnancy but also as important protection for fetal brain development in the context of prenatal opioid exposures.
Given that illicit and MOUD opioids act on the same receptors with different temporal profiles and downstream effects (Darcq and Kieffer, 2018), we expected to observe some residual effects in the PODE-T group despite MOUD exposure. The shared effects shown in Figure 2 (i.e., PODE-T and PODE-NT not significantly different) are a great example highlighting the common PODE-related alterations in functional connectivity that are not mitigated by MOUD. Moreover, although PODE-T infants showed quantitative partial normalization effects in another set of three connections (a 15.36% reduction in average difference between PODE-T and CTR compared with PODE-NT), the reduction was not enough to fully normalize the PODE-T group with respect to CTR and/or PDE groups (partly because of an interesting overcorrection effect; Fig. 4). However, when comparing PODE-T/PODE-NT with the PDE group for these three connections, PODE-T infants showed larger normalization effect toward the PDE group (i.e., 49.42% reduction in the difference between PODE-NT and PDE when comparing PODE-T with PDE). Considering that the PODE group as a whole used more drugs compared with PDE (Table 1; as discussed above), these normalization effects toward PDE are still of great significance for those affected. On the other hand, the partial normalization effects toward CTR, as observed for these three connections and the PODE-common effect (Fig. 2; i.e., no normalization observed), suggest that although MOUD may normalize certain effects associated with repeated opioid withdrawal during fetal development, the common drug effects of opioids and/or other drugs in disturbing endogenous neuroreceptor signaling are not likely fully rescued by MOUD, as one would expect. The overcorrection effects observed for these three connections are intriguing and deserve future study to characterize the underlying mechanisms, but we postulate that they may have occurred as a result of the combined effects of MOUD-related normalization coupled with the additive impacts of this other drug (i.e., MOUD) on the multidrug use profile observed in the PODE group.
Limitations
First, the drug information we currently have is qualitative in nature, limiting our ability to detect dose-dependent effects. Ideally, morphine milligram equivalents would be used to standardize exposure from different opioids. However, these data are not available at this time, and future studies are needed to further explore such dose-dependent effects. Second, the majority of the infants in our PODE-T sample were exposed to buprenorphine (buprenorphine, n = 30; methadone, n = 1). Future studies should consider the different pharmacological profiles (Byrnes and Vassoler, 2018; Volkow et al., 2019a) of various MOUD drugs as there have been mixed findings reporting different (Welle-Strand et al., 2013b) as well as similar (Kaltenbach et al., 2018) neurobehavioral outcomes associated with different MOUD opioids. Third, nine PODE infants (all PODE-T) who had neonatal abstinence syndrome (NAS) were included in the study. Of these, six infants were treated with morphine (ranging from 1 to 13 d) and three infants were treated with supportive management through the eat, sleep, console approach (Hudak et al., 2012). Future work with larger sample sizes of infants with NAS should aim to disentangle differential effects NAS may have on the developing brain. Fourth, although we were unable to fully match the groups on feeding methods after birth, the majority of infants in each group were fed breastmilk (solely or in combination with infant formula). Future studies should take this into account and examine the impact of early feeding methods on infant brain development and later outcome. Finally, the PODE-NT group had a small sample size (n = 11); this was partly driven by the fact that it is extremely difficult to engage this population for research, particularly during the pandemic. However, even with only 11 subjects, the PODE-NT group demonstrated a similar degree of alterations compared with that observed in PODE-T (n = 31) when using the sample size-dependent p value as a threshold. When we directly examined effect sizes, the PODE-NT group produced the largest effect size among all groups, supporting the notion that PODE-NT drove the opioid-related alterations, whereas MOUD provided substantial normalization of such effects.
Conclusions
To the best of our knowledge, this is the first study to reveal both normalization effects of MOUD and residual effects of prenatal opioid exposures on brain functional connectivity by directly comparing neonates prenatally exposed to opioids with MOUD and those exposed to opioids but without MOUD. Our findings indicate that MOUD conferred significant normalization effects in functional connectivity of higher order limbic and integration networks. However, significant residual effects were also observed, particularly in subcortical-frontal and corticolimbic connections. Together, among the seven connections detected in this study showing significant effects associated with the PODE-NT group when compared with CTR and/or PDE (Figs. 2–4), 14.29% of the connections (i.e., one connection) showed no normalization with respect to the CTR infants (Fig. 2), 42.86% of the connections (i.e., three connections) showed partial normalization with respect to either CTR or PDE groups (Fig. 4), and 42.86% of the connections (i.e., three connections) showed full normalization compared with the CTR infants (Fig. 3). Future work with larger sample sizes (e.g., the Health Brain and Child Development (HBCD) study (Volkow et al., 2021) is necessary to examine the full scope of PODE-related functional connectivity alterations (for both PODE-T and PODE-NT) compared with CTR/PDE to validate and extend these findings, with the ultimate goal of developing brain-based biomarkers that can be used to develop more targeted interventions to optimize neurodevelopmental outcome.
Footnotes
This work was supported by the National Institutes of Health (Grants R01DA042988 and R01DA043678 to W.G. and K.G., and R34DA050255 to W.G.) and Cedars-Sinai Precision Medicine Initiative Award and institutional support (to W.G.). We thank the families who generously gave their time to participate in this study.
The authors declare no competing financial interests.
- Correspondence should be addressed to Wei Gao at wei.gao{at}cshs.org
